In this brief talk we motivate Data Readiness Levels, an attempt to develop a language around data quality that can bridge the gap between technical solutions and decision makers such as managers and project planners.
The Data Crisis 
Anecdotally, talking to data modelling scientists. Most say they spend 80% of their time acquiring and cleaning data. This is precipitating what I refer to as the “data crisis”. This is an analogy with software. The “software crisis” was the phenomenon of inability to deliver software solutions due to increasing complexity of implementation. There was no single shot solution for the software crisis, it involved better practice (scrum, test orientated development, sprints, code review), improved programming paradigms (object orientated, functional) and better tools (CVS, then SVN, then git).
However, these challenges aren't new, they are merely taking a different form. From the computer's perspective software is data. The first wave of the data crisis was known as the software crisis.
The Software Crisis
In the late sixties early software programmers made note of the increasing costs of software development and termed the challenges associated with it as the "Software Crisis". Edsger Dijkstra referred to the crisis in his 1972 Turing Award winner's address.
The major cause of the software crisis is that the machines have become several orders of magnitude more powerful! To put it quite bluntly: as long as there were no machines, programming was no problem at all; when we had a few weak computers, programming became a mild problem, and now we have gigantic computers, programming has become an equally gigantic problem.
Edsger Dijkstra (1930-2002), The Humble Programmer
The major cause of the data crisis is that machines have become more interconnected than ever before. Data access is therefore cheap, but data quality is often poor. What we need is cheap high quality data. That implies that we develop processes for improving and verifying data quality that are efficient.
There would seem to be two ways for improving efficiency. Firstly, we should not duplicate work. Secondly, where possible we should automate work.
What I term "The Data Crisis" is the modern equivalent of this problem. The quantity of modern data, and the lack of attention paid to data as it is initially "laid down" and the costs of data cleaning are bringing about a crisis in data-driven decision making. This crisis is at the core of the challenge of technical debt in machine learning (Sculley et al. 2015).
Just as with software, the crisis is most correctly addressed by 'scaling' the manner in which we process our data. Duplication of work occurs because the value of data cleaning is not correctly recognised in management decision making processes. Automation of work is increasingly possible through techniques in "artificial intelligence", but this will also require better management of the data science pipeline so that data about data science (meta-data science) can be correctly assimilated and processed. The Alan Turing institute has a program focussed on this area, AI for Data Analytics.
Three Grades of Data Readiness 
Data-readiness describes, at its coarsest level, three separate stages of data graduation.
- Grade C - accessibility
- Transition: data becomes electronically available
- Grade B - validity
- Transition: pose a question to the data.
- Grade A - usability
The important definitions are at the transition. The move from Grade C data to Grade B data is delimited by the electronic availability of the data. The move from Grade B to Grade A data is delimited by posing a question or task to the data (Lawrence 2017).
To avoid the data crisis we need to surface the problems with management and curation of data to decision makers. Data readiness levels are a language for bringing the quality of data to the attention of decision makers so that the right levels of investment in infrastructure are raised.
Lawrence, Neil D. 2017. “Data Readiness Levels.” arXiv.
Sculley, D., Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison. 2015. “Hidden Technical Debt in Machine Learning Systems.” In Advances in Neural Information Processing Systems 28, edited by Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, 2503–11. Curran Associates, Inc. http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf.